How Effective is My Production? Achieving less machine downtime through OEE analysis

In order to meet the demands of the market for speed and adherence to schedules while at the same time increasing cost efficiency, there is often no way around the optimization of OEE (Overall Equipment Effectiveness). To do this, the OEE must first be determined and their influencing variables displayed in order to easily identify causes for deviations and optimization options and to be able to initiate measures directly or even automatically. But what sounds simple in theory often presents companies with challenges in practice, because heterogeneous machine landscapes and individual processes make it difficult to use quickly available standard software.

How can production be optimized, where can improvement opportunities be found, which processes may even indirectly slow down operative business from the background? Overall Equipment Effectiveness (GAE) or Overall Equipment Effectiveness (OEE) has established itself as the key performance indicator for this. It describes the measure of the value added of a certain plant and can therefore assume the values 0 to 1 or 0 % to 100 %. The OEE is calculated from the product of availability factor, performance factor and quality factor, whereby the terms contain both the time aspect and the quantity aspect of production (see Fig. 1). The goal of any OEE optimization is to find out the reasons for the deviation of the OEE factors from 100 %, i.e. why it was not possible to

100 % of the operating time is produced,

100% of the planned cycle time achieved,

100 % of the products are produced in the defined quality?

Fig. 1: Simplified representation of OEE calculation in production

Determining the OEE

In order to identify optimization possibilities for increasing the OEE, the first step is to calculate the OEE. A central challenge here is to bring together all the required data from production in digital form so that it can be calculated and analyzed by software. This includes, for example, the planned operating time, unplanned shutdowns, target quantity, actual quantity and good quantity. The data for this can come from various sources: In many places, existing digital infrastructures such as databases, file systems or Manufacturing Execution Systems (MES) can be used. In addition, additional data is often required, which must first be extracted from the heterogeneous landscape of many different controllers, industrial PCs and sensors.

Since the individual components within this heterogeneous structure use different protocols for data transmission, the data must first be standardized using so-called connectors. Depending on the application, this requires either additional hardware such as gateways, which access and standardize the data at defined interfaces, or software connectors, which are installed on the corresponding PCs or controllers and directly retrieve the relevant data. The harmonized data is then transmitted to the OEE software via a standardized protocol. To enable the software to calculate the OEE accurately and meaningfully, companies must define in advance which data should be included in the calculation. For example, no data may be available regarding the correctly produced parts, but this value can be determined from the combination of other production data (e.g. nominal turning angle of a screwdriver not reached Screw connection "not OK").

Downtimes also often lead to an increased need for discussion, since companies have to define for each of their systems what is a planned and what is an unplanned failure. For example, the planned setup and maintenance times require upper limits so that exceeding these times can be categorized as unplanned downtime. The software must also be able to distinguish between different downtimes. This is the only way to correctly calculate the availability factor later on.

As comprehensible as OEE analyses and optimization are in theory, they are challenging for many manufacturing companies in practice. Especially the already mentioned acquisition of the necessary data is often the crucial point between success and failure due to the heterogeneous machine landscapes and parallel running software systems. In fact, there is no reason to reinvent the wheel here - it simply has to be made round. There is already standard software on the market that has clear advantages over new developments with a short time-to-market and low costs. One example is the Nexeed Production Performance Manager from Bosch Connected Industry. It has standardized interfaces that can be used to standardize various data sources and integrate them into the overall system. This transfer takes place via a special, production-specific and open protocol. Unlike the widely used OPC UA, the focus is not on machine to machine communication (M2M), but on communication between machines and IoT solutions. In addition, the Nexeed Production Performance Manager visualizes the data sets in the first customizable detailed evaluations, which then serve as a basis for discussion of anomalies in production.

All standard products have in common that they contain the necessary standard options for the recording, standardization and evaluation of the necessary parameters, but that company-specific process knowledge is required for the OEE analysis, which must be mapped in the software. It is therefore obvious to implement individually developed additional modules on the basis of expandable standard software, which determine the OEE precisely according to the requirements of the company and use the data from the standard software for this purpose (see Fig. 2). At the same time, the OEE modules can access the functions of the standard software. If the OEE changes, for example, the raw data can be checked down to the smallest detail in order to locate the cause. Existing ticket systems can also be used to automatically distribute tasks to the relevant employees based on defined values from production. This significantly shortens machine downtimes, as the response time in the event of deviations from standards is minimized.

Fig. 2: Use of an expandable standard software for OEE analysis using the example of the Nexeed Production Performance Manager, which consolidates and provides the heterogeneous data from production.